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A solution to the learning dilemma for recurrent networks of spiking neurons

Author

Listed:
  • Guillaume Bellec

    (Graz University of Technology)

  • Franz Scherr

    (Graz University of Technology)

  • Anand Subramoney

    (Graz University of Technology)

  • Elias Hajek

    (Graz University of Technology)

  • Darjan Salaj

    (Graz University of Technology)

  • Robert Legenstein

    (Graz University of Technology)

  • Wolfgang Maass

    (Graz University of Technology)

Abstract

Recurrently connected networks of spiking neurons underlie the astounding information processing capabilities of the brain. Yet in spite of extensive research, how they can learn through synaptic plasticity to carry out complex network computations remains unclear. We argue that two pieces of this puzzle were provided by experimental data from neuroscience. A mathematical result tells us how these pieces need to be combined to enable biologically plausible online network learning through gradient descent, in particular deep reinforcement learning. This learning method–called e-prop–approaches the performance of backpropagation through time (BPTT), the best-known method for training recurrent neural networks in machine learning. In addition, it suggests a method for powerful on-chip learning in energy-efficient spike-based hardware for artificial intelligence.

Suggested Citation

  • Guillaume Bellec & Franz Scherr & Anand Subramoney & Elias Hajek & Darjan Salaj & Robert Legenstein & Wolfgang Maass, 2020. "A solution to the learning dilemma for recurrent networks of spiking neurons," Nature Communications, Nature, vol. 11(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-17236-y
    DOI: 10.1038/s41467-020-17236-y
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    Cited by:

    1. Nicholas Alonso & Jeffrey L. Krichmar, 2024. "A sparse quantized hopfield network for online-continual memory," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Melika Payvand & Filippo Moro & Kumiko Nomura & Thomas Dalgaty & Elisa Vianello & Yoshifumi Nishi & Giacomo Indiveri, 2022. "Self-organization of an inhomogeneous memristive hardware for sequence learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    3. Barbara Feulner & Matthew G. Perich & Raeed H. Chowdhury & Lee E. Miller & Juan A. Gallego & Claudia Clopath, 2022. "Small, correlated changes in synaptic connectivity may facilitate rapid motor learning," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    4. Hanle Zheng & Zhong Zheng & Rui Hu & Bo Xiao & Yujie Wu & Fangwen Yu & Xue Liu & Guoqi Li & Lei Deng, 2024. "Temporal dendritic heterogeneity incorporated with spiking neural networks for learning multi-timescale dynamics," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    5. Michalis Pagkalos & Spyridon Chavlis & Panayiota Poirazi, 2023. "Introducing the Dendrify framework for incorporating dendrites to spiking neural networks," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Zhiwei Chen & Wenjie Li & Zhen Fan & Shuai Dong & Yihong Chen & Minghui Qin & Min Zeng & Xubing Lu & Guofu Zhou & Xingsen Gao & Jun-Ming Liu, 2023. "All-ferroelectric implementation of reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    7. Rohit Abraham John & Yiğit Demirağ & Yevhen Shynkarenko & Yuliia Berezovska & Natacha Ohannessian & Melika Payvand & Peng Zeng & Maryna I. Bodnarchuk & Frank Krumeich & Gökhan Kara & Ivan Shorubalko &, 2022. "Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    8. Matteo Saponati & Martin Vinck, 2023. "Sequence anticipation and spike-timing-dependent plasticity emerge from a predictive learning rule," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    9. Francesco Barchi & Luca Zanatta & Emanuele Parisi & Alessio Burrello & Davide Brunelli & Andrea Bartolini & Andrea Acquaviva, 2021. "Spiking Neural Network-Based Near-Sensor Computing for Damage Detection in Structural Health Monitoring," Future Internet, MDPI, vol. 13(8), pages 1-22, August.

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